Semi-supervised and weakly-supervised methods constitute a large and diverse body of techniques [1]}, [2]}. In brief, these methods aim at utilizing a mix of labeled and unlabeled data or data that have not been labeled in detail. These methods have also been used in deep learning-based medical image segmentation with relative success, as in [3]}, [4]}. One possibility is to use other, less accurate, automatic methods to generate approximate segmentations on large corpora of medical images and use those to train a more accurate CNN-based segmentation model [5]}, [6]}. In general, the applicability and success of semi-supervised methods to a specific task is not certain. It has been recently argued, and experimentally demonstrated, that the gains that have been reported by many semi-supervised methods may need to be reassessed [7]}.
